Machine Learning Made Easy

Shashank Prasanna, MathWorks

Machine learning is ubiquitous. From medical diagnosis, speech, and handwriting recognition to automated trading and movie recommendations, machine learning techniques are being used to make critical business and life decisions every moment of the day. Each machine learning problem is unique, so it can be challenging to manage raw data, identify key features that impact your model, train multiple models, and perform model assessments.

In this session we explore the fundamentals of machine learning using MATLAB. Through several examples we review typical workflows for both supervised learning (classification) and unsupervised learning (clustering).

Highlights include

Accessing, exploring, analyzing, and visualizing data in MATLAB

Using the Classification Learner app and functions in the Statistics and Machine Learning Toolbox to perform common machine learning tasks such as:

About the Presenter: Shashank Prasanna is a product marketing manager at MathWorks, where he focuses on MATLAB and add-on products for statistics, machine learning, and data analytics. Prior to joining MathWorks, Shashank worked on software design and development at Oracle. Shashank holds an M.S. in electrical engineering from Arizona State University.